
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
import seaborn as sns
import datetime
import matplotlib.dates as mdates
from pyproj import Transformer
pd.set_option('display.max_columns', None)


crime = pd.read_csv('data/crime/crime.csv', low_memory=False)
crime.head()


### set date as datetime
crime.ReportedDateTime = pd.to_datetime(crime.ReportedDateTime)


%%time
transformer = Transformer.from_crs('epsg:32054', 'epsg:4326')
xx, yy = transformer.transform(crime.RoughX.values, crime.RoughY.values)

CPU times: user 1.43 s, sys: 82.7 ms, total: 1.51 s
Wall time: 1.58 s


crime['lat'] = xx
crime['lon'] = yy


### need to convert to web mercator for holoviews
from datashader.utils import lnglat_to_meters
crime.loc[:,'x'], crime.loc[:,'y'] = lnglat_to_meters(crime.lon, crime.lat)


crime.head()


crime.ReportedDateTime.max()

Timestamp('2021-07-09 06:30:00')


pre_crime = crime[(crime.ReportedDateTime >= '2018-03-25') &
                 (crime.ReportedDateTime <= '2019-07-09')].copy(deep=True)
post_crime = crime[crime.ReportedDateTime >= '2020-03-25'].copy(deep=True)


from datashader import transfer_functions as tf
from datashader.colors import Hot
from colorcet import fire
from holoviews.element.tiles import CartoDark
from holoviews.operation.datashader import datashade
import holoviews as hv
from holoviews import opts
hv.extension('bokeh')


post_auto_points = hv.Points(post_crime[post_crime.VehicleTheft>0], ['x', 'y'])
post_violent_points = hv.Points(post_crime[post_crime.Violent>0], ['x', 'y'])
post_property_points = hv.Points(post_crime[post_crime.Property>0], ['x', 'y'])
all_violent_points = hv.Points(crime[crime.Violent>0], ['x', 'y'])
all_property_points = hv.Points(crime[crime.Property>0], ['x', 'y'])


x_range, y_range = (-9805000, -9777000), (5315000, 5325000)


map_tiles  = CartoDark().opts(alpha=1, width=450, height=600, bgcolor='black', xlim=x_range, ylim=y_range)
agg = datashade(all_violent_points, x_sampling=1, y_sampling=1, cmap=Hot)
map_tiles * agg


agg = datashade(all_property_points, x_sampling=1, y_sampling=1, cmap=Hot)
map_tiles * agg


agg = datashade(post_violent_points, x_sampling=1, y_sampling=1, cmap=fire)
map_tiles * agg


agg = datashade(post_property_points, x_sampling=1, y_sampling=1, cmap=fire)
map_tiles * agg


agg = datashade(post_auto_points, x_sampling=1, y_sampling=1, cmap=Hot, width=450, height=500)
map_tiles * agg


import folium
from folium.plugins import MarkerCluster, HeatMap


homicides = crime[(crime.Homicide > 0) &
                  (crime.lat.notnull()) &
                 (crime.ReportedDateTime >= '2020-03-25')].copy(deep=True)

### for apples-to-apples comparison, using same 15 month period starting in 2018
homicides_pre = crime[(crime.Homicide > 0) &
                  (crime.lat.notnull()) &
                 (crime.ReportedDateTime >= '2018-03-25') &
                (crime.ReportedDateTime <= '2019-07-09')].copy(deep=True)


### add cleaner date fields for popups
homicides['date'] = homicides.ReportedDateTime.astype('str').str.slice(0,10)
homicides_pre['date'] = homicides_pre.ReportedDateTime.astype('str').str.slice(0,10)


crime_map_pre = folium.Map(
    location = [43.072, -87.963],
    zoom_start=11.5,
    tiles='Stamen Terrain',
    width=550,
    height=830
)

for _,row in homicides_pre.iterrows():
    folium.CircleMarker(
        location=[row['lat'], row['lon']],
        radius=1,
        color='red',
        popup=folium.Popup(f"""<strong>Date:</strong> {row['date']} <br>
                                <strong>Location: </strong> {row['Location']} <br>
                                <strong>Weapon used: </strong> {row['WeaponUsed']}
                    """, max_width=1000),
    ).add_to(crime_map_pre)

murders_pre = homicides_pre[['lat', 'lon']].values

crime_map_pre.add_child(HeatMap(murders_pre, radius=10))

### save to file
crime_map_pre.save('pre-covid-homicides.html')
crime_map_pre


crime_map = folium.Map(
    location = [43.072, -87.963],
    zoom_start=11.5,
    tiles='Stamen Terrain',
    width=550,
    height=830
)

for _,row in homicides.iterrows():
    folium.CircleMarker(
        location=[row['lat'], row['lon']],
        radius=1,
        color='red',
        popup=folium.Popup(f"""<strong>Date:</strong> {row['date']} <br>
                                <strong>Location: </strong> {row['Location']} <br>
                                <strong>Weapon used: </strong> {row['WeaponUsed']}
                    """, max_width=1000),
    ).add_to(crime_map)

murders = homicides[['lat', 'lon']].values

crime_map.add_child(HeatMap(murders, radius=10))
crime_map.save('post-covid-homicides.html')
crime_map


plt.style.use('fivethirtyeight')


crime_pre = crime[(crime.ReportedDateTime >= '2019-03-25') &
                   (crime.ReportedDateTime <= '2020-03-25')].copy(deep=True)

crime_post = crime[(crime.ReportedDateTime >= '2020-03-25') &
                   (crime.ReportedDateTime <= '2021-03-25')].copy(deep=True)

### add columns for non-auto property crimes
crime_pre['PropertyOther'] = crime_pre.Property - crime_pre.VehicleTheft
crime_post['PropertyOther'] = crime_post.Property - crime_post.VehicleTheft


tract_pre_totals = crime_pre.groupby('TRACT')\
                [['Violent', 'Homicide', 'PropertyOther', 'VehicleTheft']].sum()
tract_post_totals = crime_post.groupby('TRACT')\
                [['Violent', 'Homicide', 'PropertyOther', 'VehicleTheft']].sum()


sns.regplot(x=tract_pre_totals['Violent'],
               y=tract_post_totals['Violent'] - tract_pre_totals['Violent'])
plt.title('Pre-pandemic violent crimes vs. post-pandemic change', fontsize=14)
plt.xlabel('Pre-pandemic crimes', fontsize=14)
plt.ylabel('Post-pandemic change in crimes', fontsize=14)
plt.show()


sns.regplot(x=tract_pre_totals['VehicleTheft'],
               y=tract_post_totals['VehicleTheft'] - tract_pre_totals['VehicleTheft'])
plt.title('Pre-pandemic auto thefts vs. post-pandemic change', fontsize=14)
plt.xlabel('Pre-pandemic auto thefts', fontsize=14)
plt.ylabel('Post-pandemic change in thefts', fontsize=14)
plt.show()


sns.regplot(x=tract_pre_totals['PropertyOther'],
               y=tract_post_totals['PropertyOther'] - tract_pre_totals['PropertyOther'])
plt.title('Pre-pandemic property crimes vs. post-pandemic change', fontsize=14)
plt.xlabel('Pre-pandemic crimes', fontsize=14)
plt.ylabel('Post-pandemic change in crimes', fontsize=14)
plt.show()


tract_pre_totals[tract_pre_totals.PropertyOther > 350].index

Float64Index([186300.0], dtype='float64', name='TRACT')


tract_pre_property = tract_pre_totals[tract_pre_totals.index != 186300.0].PropertyOther
tract_post_property = tract_post_totals[tract_post_totals.index != 186300.0].PropertyOther


sns.regplot(x=tract_pre_property,
               y=tract_post_property - tract_pre_property)
plt.title('Pre-pandemic property crimes vs. post-pandemic change', fontsize=14)
plt.xlabel('Pre-pandemic crimes', fontsize=14)
plt.ylabel('Post-pandemic change in crimes', fontsize=14)
plt.show()


tract_pre_totals.sum()

Violent          15077.0
Homicide           100.0
PropertyOther    15656.0
VehicleTheft      3568.0
dtype: float64


tract_post_totals.sum()

Violent          16201.0
Homicide           169.0
PropertyOther    16355.0
VehicleTheft      5594.0
dtype: float64


import geopandas as gpd


wi_shape = 'data/tl_2020_55_tract/tl_2020_55_tract.shp'
wi_tracts = gpd.read_file(wi_shape)


### narrow to only tracts contained in crime
wi_tracts.head(1)


wi_tracts[wi_tracts.GEOID.str.contains('55079186100')]


tract_post_totals.head(1)


tract_pre_totals = tract_pre_totals.reset_index()
tract_post_totals = tract_post_totals.reset_index()

tract_pre_totals['GEOID'] = tract_pre_totals.apply(lambda x: '55079'\
                                    + str(int(x['TRACT'])).zfill(6), axis=1)
tract_post_totals['GEOID'] = tract_post_totals.apply(lambda x: '55079'\
                                    + str(int(x['TRACT'])).zfill(6), axis=1)


### Rename columns to be more explicity before merging the two
tract_pre_totals = tract_pre_totals.rename(columns={
                                            'Violent': 'Violent-pre',
                                            'PropertyOther': 'Property-pre',
                                            'Homicide': 'Homicide-pre',
                                            'VehicleTheft': 'Auto theft-pre'
                                            })

tract_post_totals = tract_post_totals.rename(columns={
                                            'Violent': 'Violent-post',
                                            'PropertyOther': 'Property-post',
                                            'Homicide': 'Homicide-post',
                                            'VehicleTheft': 'Auto theft-post'
                                            })


tract_post_totals.head()


## drop unneeded columns
tract_pre_totals.drop('TRACT', axis=1, inplace=True)
tract_post_totals.drop('TRACT', axis=1, inplace=True)


tract_totals = tract_pre_totals.merge(tract_post_totals, on='GEOID')
tract_totals.head()


tract_totals['Violent change'] = tract_totals['Violent-post'] - tract_totals['Violent-pre']
tract_totals['Homicide change'] = tract_totals['Homicide-post'] - tract_totals['Homicide-pre']
tract_totals['Property change'] = tract_totals['Property-post'] - tract_totals['Property-pre']
tract_totals['Auto theft change'] = tract_totals['Auto theft-post'] - tract_totals['Auto theft-pre']
tract_totals.head()


mke = wi_tracts.merge(tract_totals, on='GEOID', how='inner')


pop = pd.read_csv('data/census/tract-population.csv', skiprows=1)
pop.head(1)


income = pd.read_csv('data/census/tract-income.csv', skiprows=1)
income.head(1)


### create a GEOID column and narrow to only GEOID and population
pop['GEOID'] = pop.id.str.slice(-11,)
pop = pop.rename(columns={'Estimate!!Total': 'population'})

pop = pop[['GEOID', 'population']]

pop.head(1)


income['GEOID'] = income.id.str.slice(-11,)
income = income.rename(columns={'Estimate!!Median income (dollars)!!HOUSEHOLD INCOME BY RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER!!Households': 'income'})

income = income[['GEOID', 'income']]


mke = mke.merge(pop, on='GEOID', how='inner')\
        .merge(income, on='GEOID', how='inner')


for crm in ['Violent', 'Property', 'Auto theft', 'Homicide']:
    for time in ['-pre', '-post']:
        mke[crm + time + '-rate'] = (mke[crm + time] /\
                                        mke.population * 100000).round(1)


import contextily as ctx
# set appropriate projection
mke = mke.to_crs(epsg=3857)


### Looks like there's a tract with no people that needs to be removed
mke.sort_values(by='population').population[:5]

19        0
93      903
159     905
140     907
138    1053
Name: population, dtype: int64


mke = mke[mke.population >0]


## defining to use below to color pos/neg bars
heights, bins, _ = plt.hist(mke['Violent change'], bins=20)
bin_width = np.diff(bins)[0]
bin_pos = bins[:-1] + bin_width / 2
plt.close()


plt.figure(figsize=(10,4))
plt.subplot(1,2,1)
plt.hist(mke['Violent-post-rate'], bins=20, color='#74add1')
plt.title('Violent crimes per 100K by census tract', size=12)

plt.subplot(1,2,2)
mask = (bin_pos >= 0)
plt.bar(bin_pos[mask], heights[mask], width=bin_width, color='#f46d43')
plt.bar(bin_pos[~mask], heights[~mask], width=bin_width, color='#74add1')
plt.title('Post-COVID change in violent crimes by tract', size=12)
plt.show()


from matplotlib.colors import LinearSegmentedColormap, ListedColormap

violent_bins = np.quantile(mke['Violent-post-rate'], [.125, .25, .375, .5, .625, .75, .875])

colors = ['#f46d43','#fdae61','#fee090','#ffffff',#'#ffffbf',
                       '#e0f3f8','#abd9e9','#74add1']

cmap = ListedColormap(colors[::-1])
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 15))

mke.plot(ax=ax1, alpha=0.7, edgecolor='k', lw=0.5, scheme='User_Defined',
             column='Violent-post-rate', legend=True, cmap='Purples',
            classification_kwds=dict(bins=violent_bins))
ax1.set_title('Post-COVID violent crimes per 100K', size=16, y=1.02)
ctx.add_basemap(ax1, zoom=12)

mke.plot(ax=ax2, alpha=0.7, edgecolor='k', lw=0.5, cmap=cmap,
         scheme="User_Defined", column='Violent change', legend=True,
        classification_kwds=dict(bins=[-50, -30, -10, 10, 30, 50]))
ax2.set_title('Change in num. violent crimes post-COVID', size=16, y=1.02)

ctx.add_basemap(ax2, zoom=12)


## defining to use below to color pos/neg bars
heights, bins, _ = plt.hist(mke['Property change'], bins=20)
bin_width = np.diff(bins)[0]
bin_pos = bins[:-1] + bin_width / 2
plt.close()


plt.figure(figsize=(10,4))
plt.subplot(1,2,1)
plt.hist(mke['Property-post-rate'], bins=20, color='#74add1')
plt.title('Property crimes per 100K by census tract', size=12)

plt.subplot(1,2,2)
mask = (bin_pos >= 0)
plt.bar(bin_pos[mask], heights[mask], width=bin_width, color='#f46d43')
plt.bar(bin_pos[~mask], heights[~mask], width=bin_width, color='#74add1')
plt.title('Post-COVID change in property crimes by tract', size=12)
plt.show()


property_bins = np.quantile(mke['Property-post-rate'], [.125, .25, .375, .5, .625, .75, .875])

cmap = ListedColormap(['#f46d43','#fdae61','#fee090','#ffffff',
                       '#e0f3f8','#abd9e9','#74add1'][::-1])
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 15))

mke.plot(ax=ax1, alpha=0.7, edgecolor='k', lw=0.5, scheme='User_Defined',
             column='Property-post-rate', legend=True,cmap='Purples',
            classification_kwds=dict(bins=property_bins))
ax1.set_title('Post-COVID property crimes per 100K', size=16, y=1.02)
ctx.add_basemap(ax1, zoom=12)


mke.plot(ax=ax2, alpha=0.7, edgecolor='k', lw=0.5, cmap=cmap,
         scheme="User_Defined", column='Property change', legend=True,
        classification_kwds=dict(bins=[-50, -30, -10, 10, 30, 50]))
ax2.set_title('Change in num. property crimes post-COVID', size=16, y=1.02)

ctx.add_basemap(ax2, zoom=12)


## defining to use below to color pos/neg bars
heights, bins, _ = plt.hist(mke['Auto theft change'], bins=20)
bin_width = np.diff(bins)[0]
bin_pos = bins[:-1] + bin_width / 2
plt.close()


plt.figure(figsize=(10,4))
plt.subplot(1,2,1)
plt.hist(mke['Auto theft-post-rate'], bins=20, color='#74add1')
plt.title('Auto thefts per 100K by census tract', size=12)

plt.subplot(1,2,2)
mask = (bin_pos >= 0)
plt.bar(bin_pos[mask], heights[mask], width=bin_width, color='#f46d43')
plt.bar(bin_pos[~mask], heights[~mask], width=bin_width, color='#74add1')
plt.title('Post-COVID change in auto thefts by tract', size=12)
plt.show()


auto_bins = np.round(np.quantile(mke['Auto theft-post-rate'],
                    [.125, .25, .375, .5, .625, .75, .875])).astype(int)

fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 15))

cmap = ListedColormap(['#abd9e9', '#ffffff', '#fee090', '#fc8d59'])

mke.plot(ax=ax1, alpha=0.7, edgecolor='k', lw=0.5, scheme='User_Defined',
             column='Auto theft-post-rate', legend=True, cmap='Purples',
            classification_kwds=dict(bins=auto_bins))
ax1.set_title('Post-COVID auto thefts per 100K', size=14, y=1.02)
ctx.add_basemap(ax1, zoom=12)

mke.plot(ax=ax2, alpha=0.7, edgecolor='k', lw=0.5, cmap=cmap,
         scheme="User_Defined", column='Auto theft change', legend=True,
        classification_kwds=dict(bins=[-10, 10, 30]))
ax2.set_title('Change in num. auto thefts post-COVID', size=16, y=1.02)

ctx.add_basemap(ax2, zoom=12)


autos_post = crime[(crime.VehicleTheft > 0) &
                  (crime.lat.notnull()) &
                 (crime.ReportedDateTime >= '2020-03-25')].copy(deep=True)

autos_pre = crime[(crime.VehicleTheft > 0) &
                  (crime.lat.notnull()) &
                 (crime.ReportedDateTime >= '2018-03-25') &
                (crime.ReportedDateTime <= '2019-07-09')].copy(deep=True)


import matplotlib.lines as mlines
colors = ['#ffffb2','#fed976','#feb24c','#fd8d3c','#fc4e2a', '#e31a1c','#b10026']
labels = ['>=1', '>5', '>10', '>15', '>20', '>25', '>30']

handles = []
for c, l in zip(colors, labels):
    handles.append(mlines.Line2D([], [], color=c, ls='-', label=l))


fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 14))

cmap = ListedColormap(colors)

autos_pre.plot(ax=ax1, kind='hexbin', bins=[5, 10, 15, 20, 25, 30],
               x='x', y='y', alpha=0.6,
               mincnt=1, cmap=cmap, colorbar=False, gridsize=(40,40))
ax1.set_title('Pre-COVID auto thefts', y=1.02)
ax1.get_xaxis().set_visible(False); ax1.get_yaxis().set_visible(False)
ax1.legend(handles=handles)

ctx.add_basemap(ax1, zoom=12)

autos_post.plot(ax=ax2, kind='hexbin', bins=[5, 10, 15, 20, 25, 30],
                x='x', y='y', alpha=0.6,
                mincnt=1, cmap=cmap, colorbar=False, gridsize=(40, 40))
ax2.set_title('Post-COVID auto thefts', y=1.02)
ax2.get_xaxis().set_visible(False); ax2.get_yaxis().set_visible(False)
ax2.legend(handles=handles)

ctx.add_basemap(ax2, zoom=12)
plt.tight_layout()
plt.show()


mke.income = mke.income.str.replace('-', '0').astype(float)


plt.figure(figsize=(8,4))
sns.regplot(data=mke[mke.income>0], x='income', y='Violent-post-rate')
plt.title('Post-COVID violent crime rate vs. household income', fontsize=14)
plt.xlabel('Census tract median household income')
plt.show()


plt.figure(figsize=(8,4))
sns.regplot(data=mke[mke.income>0], x='income', y='Violent change')
plt.title('Post-COVID change in violent crime vs. household income', fontsize=14)
plt.xlabel('Census tract median household income')
plt.show()


plt.figure(figsize=(8,4))
sns.regplot(data=mke[mke.income>0], x='income', y='Property-post-rate')
plt.title('Post-COVID property crime rate vs. household income', fontsize=14)
plt.xlabel('Census tract median household income')
plt.show()


plt.figure(figsize=(8,4))
sns.regplot(data=mke[mke.income>0], x='income', y='Property change')
plt.title('Post-COVID change in property crime vs. household income', fontsize=14)
plt.xlabel('Census tract median household income')
plt.show()


plt.figure(figsize=(8,4))
sns.regplot(data=mke[mke.income>0], x='income', y='Auto theft-post-rate')
plt.title('Post-COVID auto theft rate rate vs. household income', fontsize=14)
plt.xlabel('Census tract median household income')
plt.show()


plt.figure(figsize=(8,4))
sns.regplot(data=mke[mke.income>0], x='income', y='Auto theft change')
plt.title('Post-COVID change in auto thefts vs. household income', fontsize=14)
plt.xlabel('Census tract median household income')
plt.show()

	IncidentNum	ReportedDateTime	ReportedYear	ReportedMonth	Location	WeaponUsed	ALD	NSP	POLICE	TRACT	WARD	ZIP	RoughX	RoughY	AssaultOffense	CriminalDamage	LockedVehicle	Robbery	VehicleTheft	Total	Violent	Property
0	163020125	2016-10-28 19:55:00	2016	10	S 10TH ST / W GRANT ST	ROCK	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	1	0	0	0	2.0	0	2.0
1	163020126	2016-10-28 19:03:00	2016	10	7822 W CAPITOL DR	UNKNO	5.0	NaN	7.0	3400.0	75.0	53222.0	2531617.227	403569.625	0	1	0	0	0	2.0	0	2.0
2	163020129	2016-10-25 21:00:00	2016	10	6501 W OHIO AV	UNKNO	11.0	NaN	6.0	19800.0	268.0	53219.0	2536921.027	365361.083	0	0	1	0	0	2.0	0	2.0
3	163020130	2016-10-28 19:20:00	2016	10	2700 W KILBOURN AV	KNIFE	4.0	14.0	3.0	13600.0	199.0	53208.0	2548621.346	386390.229	1	0	0	1	0	4.0	2	2.0
4	163020131	2016-10-28 02:30:00	2016	10	3212 N 3RD ST	UNKNO	6.0	6.0	5.0	7000.0	122.0	53212.0	2557328.086	399243.551	0	0	0	0	1	2.0	0	2.0

	IncidentNum	ReportedDateTime	ReportedYear	ReportedMonth	Location	WeaponUsed	ALD	NSP	POLICE	TRACT	WARD	ZIP	RoughX	RoughY	AssaultOffense	CriminalDamage	LockedVehicle	Robbery	VehicleTheft	Total	Violent	Property	lat	lon	x	y
0	163020125	2016-10-28 19:55:00	2016	10	S 10TH ST / W GRANT ST	ROCK	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	1	0	0	0	2.0	0	2.0	NaN	NaN	NaN	NaN
1	163020126	2016-10-28 19:03:00	2016	10	7822 W CAPITOL DR	UNKNO	5.0	NaN	7.0	3400.0	75.0	53222.0	2531617.227	403569.625	0	1	0	0	0	2.0	0	2.0	43.089902	-88.009755	-9.797201e+06	5.325666e+06
2	163020129	2016-10-25 21:00:00	2016	10	6501 W OHIO AV	UNKNO	11.0	NaN	6.0	19800.0	268.0	53219.0	2536921.027	365361.083	0	0	1	0	0	2.0	0	2.0	42.984803	-87.993340	-9.795374e+06	5.309659e+06
3	163020130	2016-10-28 19:20:00	2016	10	2700 W KILBOURN AV	KNIFE	4.0	14.0	3.0	13600.0	199.0	53208.0	2548621.346	386390.229	1	0	0	1	0	4.0	2	2.0	43.041703	-87.947691	-9.790292e+06	5.318322e+06
4	163020131	2016-10-28 02:30:00	2016	10	3212 N 3RD ST	UNKNO	6.0	6.0	5.0	7000.0	122.0	53212.0	2557328.086	399243.551	0	0	0	0	1	2.0	0	2.0	43.076366	-87.913928	-9.786534e+06	5.323603e+06

	TRACT	Violent-post	Homicide-post	Property-post	Auto theft-post	GEOID
0	0.0	0	0	0.0	2	55079000000
1	101.0	164	0	183.0	41	55079000101
2	102.0	106	1	159.0	44	55079000102
3	201.0	207	0	169.0	63	55079000201
4	202.0	114	0	133.0	31	55079000202

	Violent-pre	Property-pre	Auto theft-pre	GEOID	Violent-post	Homicide-post	Property-post	Auto theft-post
0	1	2.0	3	55079000000	0	0	0.0	2
1	163	138.0	28	55079000101	164	0	183.0	41
2	84	162.0	19	55079000102	106	1	159.0	44
3	156	131.0	26	55079000201	207	0	169.0	63
4	107	111.0	18	55079000202	114	0	133.0	31

Read in crime data and convert x/y to appropriate projection¶

Make point maps¶

Homicide heat maps¶

Pre-COVID homicides¶

Post-COVID homicides¶

Comparison of year-over-year census tract trends¶

Census tract-level maps¶

Read in tract file¶

Join tract with crime data (which will help filter to only MKE tracts)¶

Choropleth maps¶

Auto theft hexbin plots¶

Compare crime rates against incomes¶

	Violent-pre	Property-pre	Auto theft-pre	GEOID	Violent-post	Homicide-post	Property-post	Auto theft-post	Violent change	Homicide change	Property change	Auto theft change
0	1	2.0	3	55079000000	0	0	0.0	2	-1	0	-2.0	-1
1	163	138.0	28	55079000101	164	0	183.0	41	1	0	45.0	13
2	84	162.0	19	55079000102	106	1	159.0	44	22	1	-3.0	25
3	156	131.0	26	55079000201	207	0	169.0	63	51	0	38.0	37
4	107	111.0	18	55079000202	114	0	133.0	31	7	0	22.0	13